2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
Block Rotation is All You Need for MXFP4 Quantization
Yuantian Shao, Peisong Wang, Yuanteng Chen +3
Large language models (LLMs) have achieved remarkable success, but their rapidly growing scale imposes prohibitive costs in memory, computation, and energy. Post-training quantizat…
Ban&Pick: Enhancing Performance and Efficiency of MoE-LLMs via Smarter Routing
Yuanteng Chen, Peisong Wang, Yuantian Shao +3
Sparse Mixture-of-Experts (MoE) has become a key architecture for scaling large language models (LLMs) efficiently. Recent fine-grained MoE designs introduce hundreds of experts pe…
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
Zhenyu Wen, Wanglei Feng, Di Wu +6
Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Altho…
Accelerated Cloud for Artificial Intelligence (ACAI)
Dachi Chen, Weitian Ding, Chen Liang +3
Training an effective Machine learning (ML) model is an iterative process that requires effort in multiple dimensions. Vertically, a single pipeline typically includes an initial E…